Meeting Title: Brainforge x Default Weekly Sync Date: 2026-02-12 Meeting participants: Scratchpad Notetaker, Demilade Agboola, Mustafa Raja, Nandika Jhunjhunwala, Greg Stoutenburg, Caitlyn Vaughn, Lev Katreczko
WEBVTT
1 00:00:19.010 ⇒ 00:00:20.090 Nandika Jhunjhunwala: Hello.
2 00:00:20.290 ⇒ 00:00:20.800 Greg Stoutenburg: Hey, team!
3 00:00:20.800 ⇒ 00:00:21.300 Nandika Jhunjhunwala: Edit?
4 00:00:21.300 ⇒ 00:00:22.110 Greg Stoutenburg: How’s it going?
5 00:00:23.390 ⇒ 00:00:24.500 Nandika Jhunjhunwala: Good, how are you?
6 00:00:24.920 ⇒ 00:00:26.110 Greg Stoutenburg: Doing great!
7 00:00:30.200 ⇒ 00:00:32.610 Demilade Agboola: Alright, how’s everyone doing? We’re all good.
8 00:00:39.880 ⇒ 00:00:42.040 Demilade Agboola: I see K1 accepted, okay.
9 00:00:42.600 ⇒ 00:00:45.270 Nandika Jhunjhunwala: She’s gonna join, I think she’s just running, like, a few minutes late.
10 00:00:45.660 ⇒ 00:00:46.370 Greg Stoutenburg: Hi, Caitlin.
11 00:00:46.370 ⇒ 00:00:49.350 Caitlyn Vaughn: Hi! Guys, I’m on time today.
12 00:00:49.770 ⇒ 00:00:51.510 Caitlyn Vaughn: For the first time ever.
13 00:00:52.700 ⇒ 00:00:54.220 Greg Stoutenburg: We don’t count by seconds.
14 00:00:54.220 ⇒ 00:00:55.000 Caitlyn Vaughn: Yay.
15 00:00:55.000 ⇒ 00:00:56.449 Greg Stoutenburg: One for at least 40.
16 00:00:58.150 ⇒ 00:01:00.740 Caitlyn Vaughn: Very on time for me.
17 00:01:01.830 ⇒ 00:01:02.780 Caitlyn Vaughn: Oh my gosh!
18 00:01:02.940 ⇒ 00:01:05.529 Demilade Agboola: Nice to see you, Caitlin. Hope you enjoyed your time off.
19 00:01:05.820 ⇒ 00:01:08.190 Caitlyn Vaughn: Thank you, good to see you guys too, glad to be back.
20 00:01:08.380 ⇒ 00:01:09.990 Demilade Agboola: Yes, good to hear.
21 00:01:10.540 ⇒ 00:01:16.320 Demilade Agboola: Alright, so let me just, like, share screen, and I’ll give the weekly update.
22 00:01:24.730 ⇒ 00:01:31.099 Demilade Agboola: Alright, so, high level for this week. Basically, Greg refined the Phoenix Tracking Plan.
23 00:01:31.330 ⇒ 00:01:34.920 Demilade Agboola: And has started the instrumentation of that.
24 00:01:35.260 ⇒ 00:01:39.090 Demilade Agboola: And on the data platform side.
25 00:01:40.640 ⇒ 00:01:55.159 Demilade Agboola: S3 access has been given, and we’re starting to ingest the data, so as we know today, Victor was able to get us over the line in terms of that. I have been able to test it, and the data has been able to, like, move from
26 00:01:55.170 ⇒ 00:02:04.970 Demilade Agboola: Amplitude, which was what I used to test, into Mother Dog, so we know that ingestion can be done now, and so the plan is to start to ingest the data on a daily sync, so I’m setting that up.
27 00:02:05.090 ⇒ 00:02:09.190 Demilade Agboola: For all the different, sources, and then we can start to…
28 00:02:09.389 ⇒ 00:02:15.360 Demilade Agboola: have that data in Mother Doc, and we can start to, run dbt off of that.
29 00:02:15.850 ⇒ 00:02:22.059 Demilade Agboola: So the next step, and also, yes, we’re going to test the SEMrush data.
30 00:02:22.400 ⇒ 00:02:28.260 Demilade Agboola: So we’re building a hypothesis using, like, the monthly, active users and other public
31 00:02:28.360 ⇒ 00:02:41.910 Demilade Agboola: company metrics, because, like, for some sites, we’ve realized that several shitter isn’t accurate, or doesn’t even seem to be in the ballpark of what we should… what the numbers should be. So we’re just trying to run different ways in which we can get
32 00:02:42.210 ⇒ 00:02:46.119 Demilade Agboola: an estimation of what those numbers should look like.
33 00:02:46.260 ⇒ 00:02:51.250 Demilade Agboola: And that would allow us to have better, like, Conversion metrics in that regard.
34 00:02:51.540 ⇒ 00:02:56.599 Demilade Agboola: So for the next thing in terms of, like, product analytics is we want to, like, master
35 00:02:57.050 ⇒ 00:03:00.909 Demilade Agboola: The event tracking templates, and then also, like, funnel definitions.
36 00:03:01.210 ⇒ 00:03:06.670 Demilade Agboola: And on the data platform analytics, because we’ve been cleared up with S3.
37 00:03:08.110 ⇒ 00:03:12.090 Demilade Agboola: the plan is to now begin, like, the modeling work for, like, LoRaStream.
38 00:03:12.400 ⇒ 00:03:19.139 Demilade Agboola: As well as, you know, start the customer reporting and enable end stream for, Lauren and Dina.
39 00:03:19.340 ⇒ 00:03:24.680 Demilade Agboola: So at least right now, I think we’re at the point where we can actually start, like, building on these things, and…
40 00:03:24.940 ⇒ 00:03:29.659 Demilade Agboola: If there are any issues, I will be sure to let the team know.
41 00:03:31.090 ⇒ 00:03:34.500 Demilade Agboola: So, high level, any questions or any feedback on that?
42 00:03:35.280 ⇒ 00:03:40.299 Caitlyn Vaughn: No, that’s exciting, we finally got unblocked from Victor, let’s go! S3!
43 00:03:41.460 ⇒ 00:03:44.210 Demilade Agboola: That is very exciting. That is very exciting.
44 00:03:45.220 ⇒ 00:03:53.190 Demilade Agboola: Yeah, so, like, high level, the wins this week on the data platform side, we’ve been able to connect multiple data sources to Polyatomic.
45 00:03:53.510 ⇒ 00:03:58.600 Demilade Agboola: So things like Plane, Amplitude, QuickBooks, we have them in Polyatomic.
46 00:03:59.550 ⇒ 00:04:03.129 Demilade Agboola: And now, so the data is ready to be loaded into the warehouse.
47 00:04:03.280 ⇒ 00:04:09.730 Demilade Agboola: And also we’ve been able to resolve the S3 bucket thing, so now…
48 00:04:09.910 ⇒ 00:04:11.730 Demilade Agboola: the IAM access has been…
49 00:04:13.020 ⇒ 00:04:21.009 Demilade Agboola: allowed, so that Polytomic can now ingest from that, S3 bucket, and obviously the impact is that Polytomic is ready to go.
50 00:04:21.730 ⇒ 00:04:36.759 Demilade Agboola: I do know Victor said he still wants to set up IAM access specifically to… because the way it is now, it’s more open, he wants to have it more restricted, but he would open it now, and over the weekend, he will work on ensuring that it’s just restricted to
51 00:04:36.760 ⇒ 00:04:42.730 Demilade Agboola: you know, the access that Polyatomic needs only, rather than having more open identities right now.
52 00:04:42.810 ⇒ 00:04:48.449 Demilade Agboola: So, that potentially could be an issue next week if things aren’t done properly, but I think we should be fine.
53 00:04:49.460 ⇒ 00:04:50.090 Caitlyn Vaughn: Cool.
54 00:04:51.980 ⇒ 00:04:53.350 Demilade Agboola: And then…
55 00:04:53.900 ⇒ 00:04:59.159 Demilade Agboola: For the product analytics cue-ins, I’m, like, Greg, do you want to, like, just quickly walk through this?
56 00:04:59.160 ⇒ 00:05:18.610 Greg Stoutenburg: Yep. Yeah, so with Nautica’s help, we’ve managed to document some core workflows, including default OS management, queue creation, and tables, and now we’re at the stage where we can just get those built. And, the way that we’ll get those built is a couple of different ways. There’s, of course, post hogs,
57 00:05:18.610 ⇒ 00:05:42.079 Greg Stoutenburg: automatic tracking ability, so you can do what Nautica has been doing, which is use the post hog toolbar, go to the page of the app, sort of click on something, it’ll show some code or what the element is, and then you can name it and use that in your data in posthog, and then, of course, you know, manually instrumented events, whether they’re on the front end or coming in from the back end. So that has already.
58 00:05:42.080 ⇒ 00:06:00.219 Greg Stoutenburg: That’s the way that we’re doing now, so, we can continue building those out and actually get some reports set up. And then, well, I mean, there you go, I did both updates in one. We’ll continue making progress on the event tracking plan as we go, but we’ve made, some good progress in this last week on that.
59 00:06:05.670 ⇒ 00:06:06.930 Demilade Agboola: Okay, sounds good.
60 00:06:08.340 ⇒ 00:06:14.480 Demilade Agboola: So again, just in terms of Q-ins, any, like, questions or feedback on the Q-ins this week from both streams?
61 00:06:15.230 ⇒ 00:06:25.390 Caitlyn Vaughn: No, I feel like this was a good week. I feel like we got, actually, a lot of work done. It’s been, like, leading up to now, but it’s, like, the satisfying, like, actually getting the…
62 00:06:25.470 ⇒ 00:06:38.759 Caitlyn Vaughn: the work done that we’ve been trying to do, so… seems like we’ve got some instrumentation stood up, Nanda’s, like, learning post hoc, we have S3 and everything, so… cruisin’!
63 00:06:38.930 ⇒ 00:06:39.640 Demilade Agboola: Yeah.
64 00:06:40.060 ⇒ 00:06:46.800 Demilade Agboola: Definitely agree. This has been a good week in that regard, and the plan is to continue to crush it going forward.
65 00:06:47.810 ⇒ 00:07:00.370 Greg Stoutenburg: Yeah, just one thing there. I know that we did say that we would check back in on this after you were back from being away, but Caitlin, I just wanted to say, just, like, 100% sure, we’re gonna continue instrumenting post-hog, we’re not… we’ve made the decision, is that right?
66 00:07:00.370 ⇒ 00:07:01.140 Caitlyn Vaughn: Yeah, yeah.
67 00:07:01.140 ⇒ 00:07:04.380 Greg Stoutenburg: Okay, good. Just wanted to make sure that we weren’t gonna, a week later, like, well, we were kind of thinking.
68 00:07:04.860 ⇒ 00:07:06.230 Greg Stoutenburg: Okay.
69 00:07:06.710 ⇒ 00:07:10.309 Greg Stoutenburg: Wanted to make sure to just say it. Okay, great. It’s decided, we’re going with PostDog. Sounds good.
70 00:07:10.310 ⇒ 00:07:11.240 Caitlyn Vaughn: Yeah, yeah.
71 00:07:11.240 ⇒ 00:07:11.890 Greg Stoutenburg: Keep going.
72 00:07:12.270 ⇒ 00:07:12.890 Caitlyn Vaughn: Cool.
73 00:07:13.010 ⇒ 00:07:13.610 Greg Stoutenburg: Yep.
74 00:07:15.260 ⇒ 00:07:17.569 Demilade Agboola: Also, yeah, we have some…
75 00:07:17.750 ⇒ 00:07:24.510 Demilade Agboola: SEMrush data that Mustafa’s currently working on. I know it’s work in progress. Mustafa, do you want to speak to this?
76 00:07:24.510 ⇒ 00:07:30.170 Mustafa Raja: Yeah, so, we don’t really, have direct access to some of the
77 00:07:31.660 ⇒ 00:07:50.929 Mustafa Raja: traffic data for a lot of the websites. So, what we are trying to do is we are trying to correlate the monthly active users and other metrics like that, so we can infer how the traffic should look like, and once we have a good, good implementation of that, I share that with the team.
78 00:07:51.110 ⇒ 00:07:57.549 Caitlyn Vaughn: Okay, cool. I have an update on this, which is I got through all of the…
79 00:07:57.830 ⇒ 00:08:04.850 Caitlyn Vaughn: sales cycle of SEMrush yesterday, and they quoted us with a $175,000 contract.
80 00:08:04.960 ⇒ 00:08:07.100 Caitlyn Vaughn: Which is absurd.
81 00:08:07.480 ⇒ 00:08:11.429 Caitlyn Vaughn: So, I think we are…
82 00:08:11.660 ⇒ 00:08:28.220 Caitlyn Vaughn: open to alternatives on, like, the website traffic front. It actually still might be good to, like, take this dataset. I guess I’m just… I’m assuming that SEMrush is as amazing as they are pricing themselves. And in that case, maybe…
83 00:08:28.300 ⇒ 00:08:46.459 Caitlyn Vaughn: what I would love your input on, Mustafa, is, like, some other vendors that we can look at that won’t charge us, like, nearly $200,000 to pull one single data point, data piece, because it would be good to have website traffic in the app, but, like.
84 00:08:46.520 ⇒ 00:08:49.890 Caitlyn Vaughn: Not for that much money. Yeah.
85 00:08:50.920 ⇒ 00:08:54.689 Caitlyn Vaughn: Yeah, definitely, definitely. I think that’s something we can definitely look at.
86 00:08:56.080 ⇒ 00:08:58.920 Demilade Agboola: In-house, we could just talk to different parts of the team.
87 00:08:59.080 ⇒ 00:09:12.320 Demilade Agboola: You know, different people have worked on different projects, and we can find people within the project, like, within our Brainforge team, who have an idea of, like, different, vendors, price points.
88 00:09:12.720 ⇒ 00:09:22.200 Demilade Agboola: And we can just quickly come up with, like, a quick document or matrix that you can use to be able to have an idea of, yeah, this would probably be the best tool for us going forward.
89 00:09:22.200 ⇒ 00:09:24.070 Caitlyn Vaughn: Yeah.
90 00:09:24.070 ⇒ 00:09:26.300 Demilade Agboola: Something, yeah, something we can definitely do.
91 00:09:26.470 ⇒ 00:09:28.690 Caitlyn Vaughn: Cool. Awesome. Thank you so much.
92 00:09:28.690 ⇒ 00:09:30.419 Demilade Agboola: Yeah, no problem.
93 00:09:31.180 ⇒ 00:09:36.019 Demilade Agboola: Yeah, so risks and mitigations,
94 00:09:37.100 ⇒ 00:09:40.660 Demilade Agboola: I think this is largely, like, Greg, so I think Greg can speak to it.
95 00:09:40.660 ⇒ 00:09:54.499 Greg Stoutenburg: Yeah, yeah, these are, these are familiar, and they’re things we’ve been talking about, but just sort of wanted to, you know, include it in our weekly sync. We’ll continue to be flexible and agile as, as Phoenix is sort of rolled out in stages.
96 00:09:54.500 ⇒ 00:10:18.629 Greg Stoutenburg: And the conversation that Nanaka and I have been having is, we understand that, that sort of different portions of Phoenix will be rolled out to different users at different times, and so, the plan there is to, just to make sure that everything is sort of tracked on time, so that we hit our, our, you know, the desired time frame, we’ll just continue implementing tracking as those pieces are rolled out, and
97 00:10:18.770 ⇒ 00:10:34.930 Greg Stoutenburg: And that’s fine, you know, we can do it that way, and so… but just wanted to call that out, that it’ll be coming out in stages. So, you know, some users will first begin to have their events sent into posthog, you know, at the beginning of March, some others, it’ll be 3 weeks later, and… and so on, but that’ll be okay.
98 00:10:36.310 ⇒ 00:10:36.860 Caitlyn Vaughn: Cool.
99 00:10:36.860 ⇒ 00:10:59.440 Greg Stoutenburg: AutoCapture limitations is just a matter of, you know, it’s awesome that AutoCapture makes it so easy to label custom events, but we’ll just want to make sure that we do get that engineering review, so that when we have all of the events that are being logged in posthog, we make sure that they’re targeted at the right thing, that they’re recording accurately, and do that kind of QA of whatever we put in there.
100 00:10:59.560 ⇒ 00:11:09.119 Greg Stoutenburg: And then, finally, for data sources, you know, we get a lot from auto-capture and naming, but there will also be other events that, including some that we added
101 00:11:09.120 ⇒ 00:11:32.259 Greg Stoutenburg: that Nanda suggested yesterday, sort of like confirmation events. So if a user on the front end, you know, they click a button, they click a CTA, for example, and they go through some form, and they hit enter, right? So the front end will record that the user performed this action, but sometimes there’s an error, something doesn’t work as expected, so we’ll want to add some confirmation events that will come in from the back end, and so we need to make sure that we have, all of the data sources that are needed
102 00:11:32.260 ⇒ 00:11:48.489 Greg Stoutenburg: that are being sent into posthog so that we can record those sorts of events as well. And I know there’s been a conversation about this, but the default team will provide that catalog of other events that will come from elsewhere, so we can record them in the master tracking plan and make sure that we’re collecting all the right stuff.
103 00:11:50.200 ⇒ 00:11:55.570 Caitlyn Vaughn: Cool, and we had a conversation with our, main engineer on, like, the
104 00:11:55.570 ⇒ 00:12:16.149 Caitlyn Vaughn: product table schemas back-end, so… Cool. He’s gonna figure out, like, what we should be ingesting. We’re kind of, like, the way he was explaining it, we’re kind of doing it in, like, a waterfall, like, we’re getting all of the raw events, because we moved to, like, an event-based, data model, going raw events, and then, basically filtering them, and then, like.
105 00:12:16.150 ⇒ 00:12:22.540 Caitlyn Vaughn: transition them into our different objects, so… it was like, let me figure out, like, at what point…
106 00:12:22.680 ⇒ 00:12:29.649 Caitlyn Vaughn: we should pull the data in at, and then the other question he had is around PII.
107 00:12:29.650 ⇒ 00:12:34.620 Greg Stoutenburg: If we need to, like, scrub PII before pulling it into S3.
108 00:12:35.120 ⇒ 00:12:35.660 Greg Stoutenburg: Hmm.
109 00:12:35.980 ⇒ 00:12:39.909 Greg Stoutenburg: Yeah. I’ll let, Demi take the S3 question.
110 00:12:40.900 ⇒ 00:12:45.470 Demilade Agboola: Yeah, I generally prefer, like, if we can do PII before.
111 00:12:45.870 ⇒ 00:12:46.590 Demilade Agboola: Let’s B.
112 00:12:47.020 ⇒ 00:12:57.200 Demilade Agboola: Generally, if this is data that has to go through Polyatomic, we can also have that… them do that, so whatever hits the warehouse, it already has PII, like, scrubbed out.
113 00:12:57.200 ⇒ 00:12:57.850 Caitlyn Vaughn: disrupt.
114 00:12:57.850 ⇒ 00:13:06.340 Demilade Agboola: So that way, whatever hits the warehouse and whatever, like, transformations we’re doing, there are no, like, emails or, like, identifiable information that we can.
115 00:13:06.910 ⇒ 00:13:09.020 Demilade Agboola: This is who this customer is.
116 00:13:09.020 ⇒ 00:13:13.759 Caitlyn Vaughn: Yeah. So yes, we can do that with polyatomic, or we can also just do that directly.
117 00:13:13.920 ⇒ 00:13:16.690 Demilade Agboola: Before, like, looting it into the warehouse from.
118 00:13:17.480 ⇒ 00:13:24.419 Demilade Agboola: If your team wants to do it. But I think with Polyatomic, we can do that without having to put additional strain on the engineering team.
119 00:13:24.780 ⇒ 00:13:35.700 Caitlyn Vaughn: Okay, cool. Yeah, because we have, like… I think it’s fine to have our customers’ data in there, but, like, our customers’ customers’ data, we probably want to scrub out, at least.
120 00:13:35.940 ⇒ 00:13:36.680 Demilade Agboola: Yeah.
121 00:13:36.820 ⇒ 00:13:41.659 Caitlyn Vaughn: Yeah, I mean, it would be nice to have that too, but not trying to get sued, so… Yeah.
122 00:13:41.660 ⇒ 00:13:45.869 Demilade Agboola: I think, yeah, we just need, like, the criterion which you want to apply PIA on.
123 00:13:46.850 ⇒ 00:13:49.810 Demilade Agboola: I think, for instance, maybe,
124 00:13:50.280 ⇒ 00:13:56.800 Demilade Agboola: certain details might not be necessary. Yeah. So, like, maybe addresses may not be necessary, or, like.
125 00:13:57.420 ⇒ 00:14:03.770 Demilade Agboola: Certain email addresses might also not be necessary. If we know what domain they’re from and all that stuff, we don’t need.
126 00:14:04.490 ⇒ 00:14:12.420 Demilade Agboola: eggliner at default.com, we just need to know, this is default.com, a customer from default.com.
127 00:14:12.650 ⇒ 00:14:23.710 Demilade Agboola: You know, things like that. So once we just kind of have an idea of what you want to scrub out, what you want to keep in, yeah, we’ll just apply that to the ingestion so that the data doesn’t have to even hit the warehouse in the first place.
128 00:14:23.920 ⇒ 00:14:39.980 Caitlyn Vaughn: Okay, in that case, then, I’ll probably just ask for, like, a… like, a raw export from our engineer, because that’ll probably be the fastest and the easiest, like, internally to get done, and then if we can remove that PII through Polytomic, then that’s fine, we can do that there.
129 00:14:40.380 ⇒ 00:14:40.970 Demilade Agboola: Okay.
130 00:14:41.600 ⇒ 00:14:42.470 Demilade Agboola: Alright, then.
131 00:14:42.470 ⇒ 00:14:51.269 Greg Stoutenburg: And just even more broadly, if there’s a PII policy at default that you’re able to share, we can just make sure that everything that we’re putting together follows that policy for you all.
132 00:14:51.270 ⇒ 00:15:08.089 Caitlyn Vaughn: I wish we… I wish it was that simple that I could just, like, send one over. We are currently in the process of registering as a data broker so that we can be, like, a data reseller. I think right now we are a controller.
133 00:15:08.090 ⇒ 00:15:14.969 Caitlyn Vaughn: And we want to move to, like, a sub-processor model, so there’s, like, a ton of legal stuff going on in the backend, and…
134 00:15:15.210 ⇒ 00:15:28.579 Caitlyn Vaughn: We honestly were just, like, we were very strict before, and we’re trying to, like, actually loosen it a little bit, because it doesn’t really make sense to be as scrutinous as we were with, like, where we’re at. So, we’re trying to, like, find a balance.
135 00:15:28.580 ⇒ 00:15:36.430 Greg Stoutenburg: Yeah, no, I understand that, and I’ve… I had to navigate those conversations internally at Stack Overflow, because, you know, it was an engineering-built org, and they’re like.
136 00:15:36.880 ⇒ 00:15:42.800 Greg Stoutenburg: They’re like, turn off the cookies, use that VPN, like, we don’t want anyone knowing anything. It’s like, hey, hey, hey, we’re also a business.
137 00:15:42.800 ⇒ 00:15:43.270 Caitlyn Vaughn: Yeah.
138 00:15:43.270 ⇒ 00:15:51.389 Greg Stoutenburg: That’s actually maybe a good thing if the product manager can email someone who ran into an error and ask about their experience. It’s not a marketing email.
139 00:15:51.390 ⇒ 00:15:52.050 Caitlyn Vaughn: Right.
140 00:15:52.050 ⇒ 00:16:01.660 Greg Stoutenburg: Yeah, no, we understand. Well, then I guess as that’s, taking shape, just, you know, let’s all stay in the loop about it so that we can, you know, deliver on what you’ve promised to customers.
141 00:16:01.660 ⇒ 00:16:06.189 Caitlyn Vaughn: Cool, that sounds good. We’ll just use best judgment for now, and adjust later.
142 00:16:06.450 ⇒ 00:16:06.960 Greg Stoutenburg: Sounds good.
143 00:16:07.280 ⇒ 00:16:08.050 Demilade Agboola: Sounds good.
144 00:16:08.240 ⇒ 00:16:11.780 Demilade Agboola: Also, I just wanted to share this as well.
145 00:16:12.410 ⇒ 00:16:14.839 Demilade Agboola: Give me one second… so this.
146 00:16:15.340 ⇒ 00:16:18.959 Demilade Agboola: So these are the data sources that we’re trying to ingest.
147 00:16:19.880 ⇒ 00:16:39.269 Demilade Agboola: So, these four up top have all been ingested, all, like, integrated into Poatomic, where the ingestion is what will happen today, tomorrow, and I’ll be setting up the daily syncs, and how frequent that would go. So, most of them would happen, every night, probably about 1AM, 2AM Eastern.
148 00:16:39.730 ⇒ 00:16:45.319 Demilade Agboola: And that would be from… that would be through Polyatomic, so these ones are fine for now.
149 00:16:45.650 ⇒ 00:16:55.179 Demilade Agboola: Stripe, there doesn’t seem to be any data in there right now, so I’m not sure, do you still want us to integrate it, or should we just keep that, like, when you set it up, and then I would integrate it?
150 00:16:55.780 ⇒ 00:16:56.680 Demilade Agboola: Right then.
151 00:16:56.840 ⇒ 00:16:58.350 Caitlyn Vaughn: Oh, there’s nothing in Stripe?
152 00:16:58.740 ⇒ 00:17:00.689 Demilade Agboola: At least not when I went in there.
153 00:17:00.800 ⇒ 00:17:04.110 Demilade Agboola: I might have to check again, but when I went in, I didn’t see anything in strike.
154 00:17:04.599 ⇒ 00:17:07.629 Caitlyn Vaughn: Okay, maybe double-check for me?
155 00:17:07.630 ⇒ 00:17:12.150 Demilade Agboola: Yeah. I would be confused if there was nothing in Stripe. I mean…
156 00:17:12.410 ⇒ 00:17:18.470 Caitlyn Vaughn: We do have, like, a few Stripe instances. The other thing that we’re having to figure out right now is, like.
157 00:17:19.859 ⇒ 00:17:38.080 Caitlyn Vaughn: the finances of, like, launching a new product and attributing, like, cost and revenue to each of them, so we’re, like, splitting that right now, and we have some, like, some lines in the sand, but I’m not sure if we’re going to have to set up, like, a separate Stripe instance for billing and, like, do all of our,
158 00:17:38.480 ⇒ 00:17:45.939 Caitlyn Vaughn: like, revenue processing through that, versus, like, just being able to build new products on top of our current Stripe instance,
159 00:17:46.550 ⇒ 00:17:52.299 Caitlyn Vaughn: But I think that you should have the, like, original one with all of our data, and if you don’t, then that’s a problem.
160 00:17:52.610 ⇒ 00:17:55.369 Demilade Agboola: Alright, so I’ll look into that, let me just put that as a note.
161 00:17:57.290 ⇒ 00:18:02.799 Demilade Agboola: Cross-track this… And I’ll let you know before EOD.
162 00:18:03.000 ⇒ 00:18:13.349 Demilade Agboola: So yes, ClickHouse, the polyatomic integration has been done, but I will still need access to, like, your ClickHouse instance. I know there isn’t anything… you mentioned there isn’t anything going on in there.
163 00:18:13.470 ⇒ 00:18:19.639 Demilade Agboola: But, yeah, but the integration has been done. I’ve been able to look into Polyatomic and see that, yeah, you can integrate Clickhouse right now.
164 00:18:19.910 ⇒ 00:18:21.790 Caitlyn Vaughn: Cool. So that’s good.
165 00:18:21.820 ⇒ 00:18:27.079 Demilade Agboola: So default, Postgres… yeah, I’m talking to Victor about this,
166 00:18:27.250 ⇒ 00:18:41.460 Demilade Agboola: And I have sent him documentation on what to do, basically, with that, like, the information I would need to be able to integrate Postgres. He hasn’t yet responded, so the idea with that is, yeah, we’re still waiting for access for the Postgres.
167 00:18:41.850 ⇒ 00:18:51.409 Demilade Agboola: And then Hyperline, the… well… well, we’ll need to push Polyatomic for that, because they kind of stopped, because they had integrated at ClickHouse.
168 00:18:51.780 ⇒ 00:18:53.229 Demilade Agboola: They were like, well.
169 00:18:53.410 ⇒ 00:19:02.000 Demilade Agboola: Polytomic still needs S3 access, so until we get that over the line, we don’t want to, like, put more resources into creating a new connector.
170 00:19:02.710 ⇒ 00:19:03.560 Demilade Agboola: So…
171 00:19:03.660 ⇒ 00:19:11.240 Demilade Agboola: At least now that that’s been done today, we can reach out to them and say, hey, can you push Hyperline Connector for others? I’ll be doing that as well today.
172 00:19:12.060 ⇒ 00:19:24.439 Demilade Agboola: And so, like, once that’s… once we get them to start working on that, we would have Hyperline Connector created. Cool. I think, in terms of what we need right now, I think that’s largely it.
173 00:19:25.020 ⇒ 00:19:31.379 Demilade Agboola: I think we can also start thinking about factors.ai, like, just further down, so we can start to, like, say, hey.
174 00:19:32.030 ⇒ 00:19:38.910 Demilade Agboola: with Hyperline out the way, can we also get, like, Fractors.ai connector built for that?
175 00:19:39.770 ⇒ 00:19:45.339 Demilade Agboola: I think the rest will probably be as we need it, because I think Customer.io exists.
176 00:19:45.520 ⇒ 00:19:49.910 Demilade Agboola: Google Analytics exists, Google Sheets exists, Facebook exists.
177 00:19:50.250 ⇒ 00:19:53.060 Demilade Agboola: So when we need those, we can always connect that.
178 00:19:54.350 ⇒ 00:20:01.309 Caitlyn Vaughn: Okay, cool. I think for factors, I’m pretty sure Stan is out this week. Let me just double check.
179 00:20:01.610 ⇒ 00:20:04.120 Nandika Jhunjhunwala: Yeah, he’s out until…
180 00:20:04.510 ⇒ 00:20:13.699 Caitlyn Vaughn: I mean, I would just say until next week. So, maybe follow up with Stan on Tuesday, looks like Monday’s a holiday, on Tuesday, about the factors.
181 00:20:14.090 ⇒ 00:20:16.939 Caitlyn Vaughn: Set up and, like, getting you guys access,
182 00:20:18.720 ⇒ 00:20:21.960 Caitlyn Vaughn: Ask them for admin access when you ask as well.
183 00:20:22.290 ⇒ 00:20:24.230 Caitlyn Vaughn: Probably just one sweep.
184 00:20:27.350 ⇒ 00:20:31.380 Demilade Agboola: I believe Monday is the 17th?
185 00:20:32.330 ⇒ 00:20:33.330 Demilade Agboola: Well, Tuesday the 15th.
186 00:20:33.330 ⇒ 00:20:35.489 Caitlyn Vaughn: Monday is the 16th, Tuesday is the 7th?
187 00:20:36.070 ⇒ 00:20:36.729 Caitlyn Vaughn: Yeah, you got it.
188 00:20:39.250 ⇒ 00:20:40.710 Demilade Agboola: Okay, so there’s that.
189 00:20:42.540 ⇒ 00:20:45.385 Demilade Agboola: Okay, so I think that’s… Fine…
190 00:20:46.370 ⇒ 00:20:52.769 Demilade Agboola: Also, in terms of the Gantt chat, I think…
191 00:20:53.210 ⇒ 00:20:57.119 Demilade Agboola: Are we all fine with the flow of how we plan to go about things?
192 00:21:05.340 ⇒ 00:21:08.699 Demilade Agboola: So, first stream will be Laura’s stream, as well as,
193 00:21:09.140 ⇒ 00:21:10.969 Demilade Agboola: And on Dina’s stream, Dina’s stream?
194 00:21:11.220 ⇒ 00:21:11.840 Caitlyn Vaughn: I think…
195 00:21:11.840 ⇒ 00:21:13.939 Demilade Agboola: We’re fine with that being the order.
196 00:21:14.630 ⇒ 00:21:16.860 Demilade Agboola: And then Lev will be up next.
197 00:21:17.460 ⇒ 00:21:23.390 Demilade Agboola: And then, yeah, so Lev will be up next.
198 00:21:26.730 ⇒ 00:21:29.179 Demilade Agboola: Okay, wait, have you, on the.
199 00:21:29.230 ⇒ 00:21:34.380 Caitlyn Vaughn: customer success front, have you talked to Lauren recently?
200 00:21:35.160 ⇒ 00:21:53.529 Demilade Agboola: No, we haven’t, because, we’ve just been blocked, and the focus has just been largely getting data in. Totally fine. …data in, we have the raw numbers, we have an idea of what the data schema looks like. We can then start to put things together, and then we can come back to each stakeholder with more educated questions, rather than.
201 00:21:53.530 ⇒ 00:21:54.140 Caitlyn Vaughn: I’m just…
202 00:21:54.140 ⇒ 00:21:55.029 Demilade Agboola: Back in the dock.
203 00:21:55.440 ⇒ 00:22:17.869 Caitlyn Vaughn: Okay, perfect. So, we had… so Deanna’s out on maternity leave, but we just had Sid, who was my, like, product counterpart, basically move over to, like, head of CS for Q1, and he’s, like, redoing the entire CS motion right now, so I know he was trying to build out some stuff in equals, and I just told him to, like, go to you guys, so…
204 00:22:18.090 ⇒ 00:22:28.789 Caitlyn Vaughn: I will point him your direction. He’ll probably be your stakeholder for, like, the CS reporting kind of stuff. He has a really good idea of, like, what we need to do for that.
205 00:22:29.070 ⇒ 00:22:34.669 Demilade Agboola: Okay. Alright, sounds good then. So, I think we’ll schedule a call with him, say, Tuesday next week, or Wednesday next week?
206 00:22:34.670 ⇒ 00:22:35.750 Caitlyn Vaughn: Totally, yeah.
207 00:22:35.870 ⇒ 00:22:36.960 Demilade Agboola: Sounds good.
208 00:22:37.800 ⇒ 00:22:42.169 Demilade Agboola: Alright, just, is he in the Slack channel as well?
209 00:22:42.510 ⇒ 00:22:43.890 Caitlyn Vaughn: I’m sure he is, yeah.
210 00:22:43.890 ⇒ 00:22:51.899 Demilade Agboola: Oh, okay. Alright, that’ll be important, because that’s how we’ll be able to reach out. Alright, so I’ll be able to share this, and just kind of tell, like, next steps.
211 00:22:52.120 ⇒ 00:22:55.750 Demilade Agboola: And ask Sid for his availability next week.
212 00:22:57.020 ⇒ 00:23:01.849 Caitlyn Vaughn: We’ll see… Yeah, he’s already in the channel. Okay. Should be good.
213 00:23:01.850 ⇒ 00:23:02.390 Demilade Agboola: Sounds good, man.
214 00:23:02.390 ⇒ 00:23:08.579 Caitlyn Vaughn: Okay, and then looking at the rest, you said you’re gonna start with the financial data modeling?
215 00:23:08.580 ⇒ 00:23:17.119 Demilade Agboola: Yeah, so it’s GTM revenue metrics for LARA, and then customer reporting and enablement, so at this point will be for SID.
216 00:23:17.770 ⇒ 00:23:25.699 Demilade Agboola: And then up next will be Lev for, the customer productivity… Product Activity Dashboard.
217 00:23:26.130 ⇒ 00:23:29.339 Demilade Agboola: And then we would have stand-up next.
218 00:23:29.620 ⇒ 00:23:30.650 Demilade Agboola: Cool.
219 00:23:33.400 ⇒ 00:23:37.899 Caitlyn Vaughn: Okay, so by end… or by mid-March, we’re gonna have Stan stayed in.
220 00:23:38.600 ⇒ 00:23:44.870 Demilade Agboola: Yes, we might have to move things around, because, like, you know, we’re a bit over time with some of these things.
221 00:23:44.870 ⇒ 00:23:46.419 Caitlyn Vaughn: Because of the S3 delay.
222 00:23:46.420 ⇒ 00:23:47.209 Demilade Agboola: Yeah, because of the S3.
223 00:23:47.210 ⇒ 00:23:49.840 Caitlyn Vaughn: Okay, that’s totally fine.
224 00:23:49.840 ⇒ 00:23:53.030 Demilade Agboola: But we should be looking at, yeah, like, probably end of March.
225 00:23:53.610 ⇒ 00:23:56.550 Demilade Agboola: Worst case scenario, like, maybe a week shift for things.
226 00:23:57.180 ⇒ 00:23:57.680 Caitlyn Vaughn: Totally.
227 00:23:57.960 ⇒ 00:23:58.660 Demilade Agboola: Alright.
228 00:23:58.660 ⇒ 00:24:16.710 Caitlyn Vaughn: And then on the product data front, obviously we can’t, like, fully stand that up until we have all the Click House, you know, stood up, and, like, people actually using the new product. But we’re expected to, like, start selling it next week, and then migrating over a few customers by, hopefully, March, so…
229 00:24:16.960 ⇒ 00:24:19.660 Caitlyn Vaughn: As soon as we have, like, some people using it, then…
230 00:24:20.300 ⇒ 00:24:24.679 Caitlyn Vaughn: we should definitely prioritize, like, getting in product data so we can set up PLG.
231 00:24:25.100 ⇒ 00:24:26.260 Demilade Agboola: Alright, sounds great.
232 00:24:27.870 ⇒ 00:24:30.790 Demilade Agboola: Okay, so I think in terms of this, we’re…
233 00:24:31.020 ⇒ 00:24:34.180 Demilade Agboola: Done for, like, what we had talked about this week.
234 00:24:34.320 ⇒ 00:24:39.239 Demilade Agboola: Just, does anyone have any, like, questions, feedback, or any things that they would like to.
235 00:24:39.240 ⇒ 00:24:40.000 Nandika Jhunjhunwala: Just to, like…
236 00:24:40.390 ⇒ 00:24:41.350 Demilade Agboola: Pay attention to?
237 00:24:41.980 ⇒ 00:24:48.090 Nandika Jhunjhunwala: If you could go back to the product analytics, like, mitigation and risk slide.
238 00:24:48.430 ⇒ 00:24:53.520 Demilade Agboola: Okay… Not a problem… this.
239 00:24:54.240 ⇒ 00:24:55.220 Nandika Jhunjhunwala: Yeah.
240 00:24:55.300 ⇒ 00:25:06.869 Nandika Jhunjhunwala: So here, like, just wanted to make a distinction, like, any client-side events, even if it’s, like, a custom event, I can instrument on the codebase, and then…
241 00:25:06.870 ⇒ 00:25:16.890 Nandika Jhunjhunwala: if we are able to get, like, the product data, like, the server-side data into Post Hog, it’s also possible for me to then instrument that
242 00:25:17.350 ⇒ 00:25:29.130 Nandika Jhunjhunwala: within post-hoc, and, like, tie that into, like, the client-side events, hopefully. I think there’s, like, a possibility. So just wanted to, like, make, like, make it visible that we can try to, like.
243 00:25:29.280 ⇒ 00:25:33.730 Nandika Jhunjhunwala: Unblock on that as much as we can, so we can get this set up soon.
244 00:25:34.180 ⇒ 00:25:36.919 Nandika Jhunjhunwala: So that was, like, the only note I wanted to make.
245 00:25:36.920 ⇒ 00:25:54.829 Greg Stoutenburg: Yeah, no, good note. Yeah, the point just being, as long as we have… so we need two things, right? We want all… anything that we’re going to be tracking to be recorded in the tracking plan, so that, you know, whenever there’s a handoff or someone is onboarded, you know, we know what it is. And then, secondly, just to make sure that the source is… any source where you want.
246 00:25:54.830 ⇒ 00:26:03.540 Greg Stoutenburg: an event from that source to show up in posthog. It’s just, you know, it has to be sent in a post hog. So, yeah. But definitely, yeah, and good call out there.
247 00:26:04.450 ⇒ 00:26:12.660 Caitlyn Vaughn: Cool. One other thing that I learned from talking to our, like, data engineer yesterday, which is…
248 00:26:12.800 ⇒ 00:26:21.879 Caitlyn Vaughn: We’re moving from Postgres to ClickHouse for the new product, but we’re actually going to be storing some of our data in Postgres still.
249 00:26:22.060 ⇒ 00:26:33.390 Caitlyn Vaughn: So, apparently we’re hosting, like, configurations in Postgres, and, like, the actual data that comes through the platform, like, product data, in ClickHouse.
250 00:26:34.020 ⇒ 00:26:38.239 Caitlyn Vaughn: So I’m not sure exactly, like, what the split is and why, but…
251 00:26:38.600 ⇒ 00:26:39.030 Greg Stoutenburg: Okay.
252 00:26:39.030 ⇒ 00:26:41.000 Caitlyn Vaughn: Possibly need both sources.
253 00:26:41.240 ⇒ 00:26:43.019 Greg Stoutenburg: Okay, sounds good.
254 00:26:43.970 ⇒ 00:26:45.109 Greg Stoutenburg: We’ll note that.
255 00:26:45.780 ⇒ 00:26:46.610 Caitlyn Vaughn: Awesome.
256 00:26:47.170 ⇒ 00:26:59.410 Greg Stoutenburg: I think for me, my last thought, so I was thinking about everything that we’re doing, what the new PLG pricing and packaging looks like, and all these events that we’re trying to track, and a thought I had, just looking down the line, is,
257 00:27:00.110 ⇒ 00:27:19.260 Greg Stoutenburg: what benchmarks might be in place already, or what goals might be in place already for what successful, like, what this successful PLG motion would look like? Is that something that, that you’re able to share, that there’s been work done on, that we can look at to evaluate how this has gone once,
258 00:27:19.620 ⇒ 00:27:21.740 Greg Stoutenburg: Once we’ve got our analytics stood up.
259 00:27:22.340 ⇒ 00:27:26.399 Caitlyn Vaughn: Yes, let me pull this up. So…
260 00:27:26.710 ⇒ 00:27:33.040 Caitlyn Vaughn: I mean, essentially, we do have something, but it’s really, like, outcomes-based versus, like.
261 00:27:33.720 ⇒ 00:27:42.649 Caitlyn Vaughn: inputs based… hold on… conversion funnel, I can share this. Here’s, like, our financial goals for next year, or for this year.
262 00:27:45.640 ⇒ 00:27:50.329 Caitlyn Vaughn: Okay, so… If we go to…
263 00:27:50.990 ⇒ 00:28:00.440 Caitlyn Vaughn: Where are we? ARR from Organic, PLG, New ARR. Okay. So, here is our… basically our goals, and like.
264 00:28:00.740 ⇒ 00:28:04.049 Caitlyn Vaughn: to be honest, I’m not quite sure that we’re gonna launch until April.
265 00:28:04.050 ⇒ 00:28:07.529 Greg Stoutenburg: Yeah. So, like, there’s potential for this to move back, but…
266 00:28:07.530 ⇒ 00:28:10.290 Caitlyn Vaughn: Assuming that we launch on time, our goal is, like.
267 00:28:10.640 ⇒ 00:28:18.840 Caitlyn Vaughn: 6 customers in March, 13 in April, 15, and it goes up. So from, like, a revenue perspective, starting at…
268 00:28:19.060 ⇒ 00:28:25.650 Caitlyn Vaughn: what, like, 36, 80, 90. And eventually, our total goal for the year is, I think, like, 3.5.
269 00:28:26.650 ⇒ 00:28:40.519 Caitlyn Vaughn: So, I’m honestly, like, this looks good on paper, what I’m assuming is gonna happen is we’re gonna launch, and it’s gonna be, like, pretty painful for the first couple of months, and we’re gonna iterate and, like, figure out what’s actually gonna work, and then…
270 00:28:40.520 ⇒ 00:28:41.030 Greg Stoutenburg: Yeah.
271 00:28:41.030 ⇒ 00:28:42.460 Caitlyn Vaughn: It will kinda…
272 00:28:42.630 ⇒ 00:28:52.729 Caitlyn Vaughn: come back around, hopefully, if we’re doing our job. So I’m not, like, super worried about, like, if we don’t have 6 customers in the first, you know, month. Panic. Yeah.
273 00:28:53.110 ⇒ 00:29:10.520 Greg Stoutenburg: Yeah, okay. Yeah, and as time goes on, and, you know, Phoenix launches, and we’re seeing what we’re seeing in posthog and our other data sources, something that we can also help with is, you know, further down the line, just looking at experimentation and things that we can do to, make whatever changes need to be made so that we
274 00:29:10.520 ⇒ 00:29:16.880 Greg Stoutenburg: you know, we know that users are activating strongly, that they’re coming back to the platform, they’re finding what they’re looking for, that’s something that we can help with as well.
275 00:29:17.020 ⇒ 00:29:23.009 Greg Stoutenburg: Yeah, totally. I think I’m definitely gonna lean on you. This is, like, where I’m most excited for you to have joined the team.
276 00:29:23.300 ⇒ 00:29:27.399 Greg Stoutenburg: That’s what I actually know about, so… do it.
277 00:29:27.910 ⇒ 00:29:28.800 Greg Stoutenburg: Yes.
278 00:29:28.800 ⇒ 00:29:38.289 Caitlyn Vaughn: Yeah, we’re, like, in this leadership meeting talking through all these numbers, right? And they’re like, okay, you, 3.5 million, like, sure, yeah, I’m on it, you know. Right, right, right.
279 00:29:38.290 ⇒ 00:29:42.459 Greg Stoutenburg: Yeah, don’t scrub that PII, because everyone who comes in, you gotta call them up, you’re like, please.
280 00:29:42.460 ⇒ 00:29:44.009 Caitlyn Vaughn: Yeah, please, please give us.
281 00:29:44.010 ⇒ 00:29:44.630 Greg Stoutenburg: That’s funny.
282 00:29:44.630 ⇒ 00:29:46.220 Caitlyn Vaughn: I really need this.
283 00:29:46.220 ⇒ 00:29:48.549 Greg Stoutenburg: Yeah. Okay, sounds good. Cool.
284 00:29:49.010 ⇒ 00:29:53.079 Caitlyn Vaughn: Cool. Amazing. Alright, yeah, other than that, anything else?
285 00:29:53.660 ⇒ 00:30:09.509 Nandika Jhunjhunwala: I had, like, a… sort of, like, a data project in mind, if your team has, like, availability and time. It’s no rush at all, but this is more so, like, something that the go-to-market team has been struggling with, because we, like, index so heavily on, like.
286 00:30:09.890 ⇒ 00:30:22.820 Nandika Jhunjhunwala: employee count, and then employee count by sales team. And there’s, like, no data vendor that does a great job at accuracy for these numbers. What we’ve realized is, like, those numbers cue, like.
287 00:30:23.000 ⇒ 00:30:40.910 Nandika Jhunjhunwala: in, like, a big, big range, like, the standard deviation amongst the, like, those data vendors is so large, like, it can range from, like, a number, like, 2 to, like, 100, like, for the same company. So I was… I was talking about this, and, like, what I’m thinking is, like, if we could calculate, like.
288 00:30:40.990 ⇒ 00:30:58.979 Nandika Jhunjhunwala: industry standard for, like, size of, like, portion of, like, sales team to, like, employee count as a ratio or, like, a percentage. And then once we see, like, a big deviation from that average, in terms of the data we get, we, like, revert to, like, that standard percentage.
289 00:30:59.160 ⇒ 00:31:04.320 Nandika Jhunjhunwala: from, like, employee count. So I was wondering, like, if that’s, like, a project your team could help with?
290 00:31:04.680 ⇒ 00:31:14.540 Nandika Jhunjhunwala: like, maybe, like, a sample size of, like, 500 or 1,000, like, tech companies, and we have their employee count, and then maybe we find, like, some source of truth for, like.
291 00:31:14.780 ⇒ 00:31:19.699 Nandika Jhunjhunwala: Both employee count and sales team size, and, like, calculate, like, that ratio if possible.
292 00:31:20.180 ⇒ 00:31:23.349 Nandika Jhunjhunwala: That would be, I think, like, super helpful in terms of, like.
293 00:31:23.480 ⇒ 00:31:28.099 Nandika Jhunjhunwala: Us running a go-to-market motion and being confident in, like, indexing on that data.
294 00:31:29.160 ⇒ 00:31:29.550 Caitlyn Vaughn: I know.
295 00:31:29.550 ⇒ 00:31:30.050 Demilade Agboola: Okay.
296 00:31:30.050 ⇒ 00:31:35.140 Caitlyn Vaughn: We definitely have that data with PDL. We have,
297 00:31:35.290 ⇒ 00:31:39.039 Caitlyn Vaughn: Employee count by role, and employee size.
298 00:31:39.620 ⇒ 00:31:41.430 Nandika Jhunjhunwala: Amazon also does that,
299 00:31:41.430 ⇒ 00:31:42.290 Caitlyn Vaughn: kudos.
300 00:31:42.290 ⇒ 00:31:44.430 Nandika Jhunjhunwala: LinkedIn? LinkedIn. Yeah.
301 00:31:44.800 ⇒ 00:31:49.880 Caitlyn Vaughn: Okay, yeah, so you’re saying, like, you just want to figure out, like, the average,
302 00:31:50.180 ⇒ 00:31:55.139 Caitlyn Vaughn: like, company size to sales… Yeah. …seats. Okay.
303 00:31:55.520 ⇒ 00:31:56.690 Caitlyn Vaughn: Nice, yeah.
304 00:31:57.560 ⇒ 00:32:10.690 Demilade Agboola: Okay, and would that be… would you want that to be a flat number? Like, it… like, because I would assume that depending on the company size, that would also have an, like, a ratio.
305 00:32:11.560 ⇒ 00:32:13.870 Demilade Agboola: So, for instance, a company, say.
306 00:32:14.150 ⇒ 00:32:20.230 Demilade Agboola: Of a thousand people might have, say, a 1%, or maybe even less than 1%.
307 00:32:20.230 ⇒ 00:32:20.620 Nandika Jhunjhunwala: Yes.
308 00:32:21.240 ⇒ 00:32:26.610 Demilade Agboola: By a company of, say, 50, that ratio might be, like, 2%.
309 00:32:26.610 ⇒ 00:32:27.730 Nandika Jhunjhunwala: For sure, yeah.
310 00:32:27.730 ⇒ 00:32:32.530 Demilade Agboola: So, like, do you want that to be by buckets, or do you just want to have, like, a grand…
311 00:32:32.750 ⇒ 00:32:37.120 Demilade Agboola: Percentage that we will just apply you… Whatever needs.
312 00:32:37.120 ⇒ 00:32:56.270 Nandika Jhunjhunwala: That’s a great question, yeah. I think both would be helpful, like, one, just, like, all… all group, like, no groupings, and then second, like, grouping by company size, buckets, like that, I think. That’s a great idea, and great call-out. If possible, that would be… that would be great, yeah.
313 00:32:57.100 ⇒ 00:33:06.870 Demilade Agboola: Alright, okay, alright, that’s something we can definitely work on, just give you an idea of, like, what those percentages look like, based off the data that we can get from verifiable sources.
314 00:33:07.440 ⇒ 00:33:16.059 Demilade Agboola: And then going forward, we can then try and apply that to new data… company data as that comes in, once we have an idea of what the, employee count is.
315 00:33:16.940 ⇒ 00:33:19.800 Nandika Jhunjhunwala: Cool, yeah, that would be, like, such a big help, yeah.
316 00:33:19.800 ⇒ 00:33:22.130 Demilade Agboola: Okay, alright, we’ll definitely look at that.
317 00:33:22.370 ⇒ 00:33:25.509 Demilade Agboola: editorial, like, request and talk in-house about that.
318 00:33:25.510 ⇒ 00:33:30.589 Nandika Jhunjhunwala: Yeah. Would you like the request in writing, or anything of that sort to help, or is this…
319 00:33:32.570 ⇒ 00:33:40.250 Demilade Agboola: I think that… I think this is clear, but, like, if you feel like you can add a bit more context, like, by writing it down, you will definitely appreciate that.
320 00:33:40.500 ⇒ 00:33:45.750 Nandika Jhunjhunwala: That’s, like, the extent of what I’ve thought through this, but I’m happy to, like, send it in writing, too, yeah.
321 00:33:45.750 ⇒ 00:33:48.540 Demilade Agboola: Okay, alright, sounds good then, we’ll look out, we’ll look out for that.
322 00:33:48.710 ⇒ 00:33:49.609 Nandika Jhunjhunwala: Thank you.
323 00:33:51.980 ⇒ 00:33:57.120 Demilade Agboola: Okay, does anyone have any other, like, questions, feedback, or any other things you’d like to thrash out now?
324 00:33:58.410 ⇒ 00:34:07.169 Caitlyn Vaughn: No, I so appreciate you guys. This is, like, awesome, this huge chunk of work that we’ve been looking at forever, and you guys are really… you’re doing an amazing job.
325 00:34:07.370 ⇒ 00:34:09.399 Demilade Agboola: Okay, thank you, thank you.
326 00:34:10.170 ⇒ 00:34:11.559 Nandika Jhunjhunwala: Thank you so much.
327 00:34:11.560 ⇒ 00:34:12.000 Greg Stoutenburg: Alright?
328 00:34:12.000 ⇒ 00:34:12.600 Demilade Agboola: Alright.
329 00:34:13.090 ⇒ 00:34:14.190 Caitlyn Vaughn: Two years later.
330 00:34:14.190 ⇒ 00:34:15.650 Greg Stoutenburg: Thanks.
331 00:34:15.659 ⇒ 00:34:16.379 Caitlyn Vaughn: Bye.